Machine Learning Slides
Machine Learning (ML) is a branch of Artificial Intelligence that enables computers to learn patterns from data without being explicitly programmed. It uses algorithms to make predictions, classifications, or decisions based on past experiences.
Machine Learning (ML) is a branch of Artificial Intelligence (AI) that enables computers to learn from data without being explicitly programmed.
It uses algorithms to identify patterns, relationships, and trends in historical data.
Based on the learned patterns, ML models can make predictions or decisions on new, unseen data.
The accuracy and performance of these models improve as they are trained with more data.
Machine Learning is widely used in applications such as recommendation systems, fraud detection, healthcare, image recognition, and natural language processing.
What You'll Learn
Introduction
Introduction to Machine Learning — Concepts, Types
A visual, beginner-friendly introduction to Machine Learning covering what ML actually is, how it d…
44 minLinear Regression Explained
A visual, beginner-friendly guide to Linear Regression built around a real Mumbai flat-pricing exam…
44 minLogistic Regression Explained
A visual, beginner-friendly guide to Logistic Regression built around a real oncology case study. L…
49 minROC Curve & AUC Explained
A visual, beginner-friendly guide to the ROC Curve and AUC built around a real fraud-detection scen…
44 minCross-Validation in Machine Learning — K-Fold, Stratified, TimeSeries & Nested CV
A visual, beginner-friendly guide to Cross-Validation covering why single train-test splits lie, ho…
50 minPrecision, Recall & F1 Score Explained — With Confusion Matrix, F-Beta & MCC
A visual, beginner-friendly guide to Precision, Recall and F1 Score built around a real TB-screenin…
45 minBias, Variance, Underfitting & Overfitting
A visual, beginner-friendly guide to the bias-variance tradeoff and its two failure modes — underfi…
63 minDecision Trees Explained — Splits, Gini, Entropy, Pruning & Feature Importance
A visual, beginner-friendly guide to Decision Trees built around a loan-officer analogy and the cla…
55 minEntropy, Information Gain & Gini Impurity
A visual, beginner-friendly guide to the three metrics that drive every decision tree. Learn Shanno…
51 minRidge & Lasso Regression Explained — L1 vs L2 Regularisation & ElasticNet
A visual, beginner-friendly guide to regularised linear regression. Learn why plain OLS overfits wi…
51 minRandom Forest Explained — Bagging, OOB Score
A visual, beginner-friendly guide to Random Forest — the ensemble that turned decision trees into a…
61 minNaive Bayes Classifier Explained — Bayes' Theorem, Variants, Smoothing & Log-Space
A visual, beginner-friendly guide to Naive Bayes — the probabilistic classifier that has powered sp…
52 minSupport Vector Machines Explained — Margins, Kernels, C & γ Tuning
A visual, beginner-friendly guide to Support Vector Machines. Learn maximum-margin classification, …
54 minSVM Kernels Explained — Linear, RBF, Polynomial & The Kernel Trick
A visual deep dive into SVM kernels — the mathematical trick that lets a linear classifier curve th…
58 minK-Nearest Neighbors Explained
A visual, beginner-friendly guide to K-Nearest Neighbors — the lazy learner that classifies by aski…
51 minEnsemble Learning: Bagging, Boosting & Stacking Explained
Why do random forests and XGBoost dominate Kaggle? Because a crowd of "okay" models beats one brill…
50 minBoosting & XGBoost: From AdaBoost to Extreme Gradient Boosting
Why does XGBoost dominate tabular ML? Because boosting chains weak, shallow trees where each one fi…
57 minGradient Boosting: Learn From Mistakes, One Tree at a Time
How does gradient boosting turn shallow, weak trees into the most accurate model on tabular data? B…
51 minXGBoost Explained: The Regularized, Second-Order Boosting Engine
What actually happens inside XGBoost? This deep-dive opens the hood — the second-order Taylor objec…
52 minPrincipal Component Analysis (PCA): Reduce Dimensions, Keep the Signal
How do you squeeze 64 features into 30 — or 100 into 2 — and barely lose anything? PCA rotates your…
40 minPCA for Dimensional Reduction: The Maths, Worked by Hand
See exactly how PCA shrinks many features into a few — not just the theory, but the arithmetic. Thi…
37 minLinear Discriminant Analysis (LDA): Projecting for Maximum Class Separation
Where PCA chases variance, LDA chases separation. This supervised method finds the projection that …
43 minUnsupervised Learning: Finding Hidden Structure Without Labels
No labels, no teacher — just raw data and the goal of uncovering the structure inside it. This tuto…
46 minK-Means Clustering: Assign, Recentre, Repeat
The most-used clustering algorithm, demystified. K-Means places K centroids, assigns each point to …
42 minHierarchical Clustering: Build the Tree, Then Choose K
No need to pick K up front — hierarchical clustering merges the closest clusters over and over, bui…
43 min